A quantitative assessment of daily transportation energy demand and electrification potential across the dwelling types in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
The urban passenger transportation sector is a major energy consumer in Canada, accounting for nearly 48% of transport-related energy use and is heavily reliant on fossil fuel-based vehicles. Electrifying private vehicles offers a promising solution to reduce fossil fuel consumption and greenhouse gas (GHG) emissions, but its full potential remains underexplored. This study focuses on the Greater Toronto and Hamilton Area (GTHA). It examines how residential dwelling types influence daily household transportation energy demand, with a specific focus on electrification barriers in multi-unit residential buildings (MURBs). Using personal and household travel surveys, the study applies supervised machine learning models, such as Random Forest and Decision Tree models, to impute vehicle engine types and estimate daily transportation energy use. The results show significant variation in energy demand by dwelling type, with detached homes consuming the most, followed by MURBs and townhouses. Moreover, there is an increase in reliance on private vehicles and ridesharing post-pandemic. Scenario analysis reveals that a complete transition to electric vehicles (EVs) has the potential to reduce daily household private vehicle energy consumption by up to 77.3%. The reductions vary by dwelling type, with detached homes projected to achieve a 54.4% decrease, while MURBs are expected to see a 14.8% reduction. Peak hour charging demand in 100% EV scenarios would reach 6640 GJ for houses and 1792 GJ for MURBs. These findings underscore the need for targeted policies to promote EV adoption, particularly for MURBs, and tailored incentives for households with detached homes. • Transportation energy demand varies by dwelling type, with detached homes highest. • Daily energy demand for private vehicles and ridesharing increased post-pandemic. • 100% EVs can reduce daily private vehicle energy consumption by 77.3%. • Detached homes may achieve a 54.4% reduction in energy demand, MURBs 14.8%. • Peak hour charging demand: 6640 GJ for detached homes, 1792 GJ for MURBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".